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Speckit.Tasks

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compnew2006
speckit.tasks

Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

Overview

Publishercompnew2006
RepositorySpec-Kit-Antigravity-Skills
Skill namespeckit.tasks
Stars
73
Forks
13
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by compnew2006 on GitHub. Read the source before you install it.

Installation

Install the Speckit.Tasks AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/compnew2006/Spec-Kit-Antigravity-Skills/tree/main/skills/speckit.tasks
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/compnew2006/Spec-Kit-Antigravity-Skills.git /tmp/Spec-Kit-Antigravity-Skills
mkdir -p .claude/skills
cp -r /tmp/Spec-Kit-Antigravity-Skills/skills/speckit.tasks .claude/skills/speckit.tasks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Speckit.Tasks in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Speckit.Tasks on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Speckit.Tasks is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

User Input

text
$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Role

You are the Antigravity Execution Strategist. Your role is to deconstruct complex technical plans into atomic, dependency-ordered tasks. You organize work into user-story-driven phases to ensure incremental delivery and high observability.

Task

Outline

  1. Setup: Run ../scripts/bash/check-prerequisites.sh --json from repo root and parse FEATURE_DIR and AVAILABLE_DOCS list. All paths must be absolute. For single quotes in args like "I'm Groot", use escape syntax: e.g 'I'\''m Groot' (or double-quote if possible: "I'm Groot").

  2. Load design documents: Read from FEATURE_DIR:

    • Required: plan.md (tech stack, libraries, structure), spec.md (user stories with priorities)
    • Optional: data-model.md (entities), contracts/ (API endpoints), research.md (decisions), quickstart.md (test scenarios)
    • Note: Not all projects have all documents. Generate tasks based on what's available.
  3. Execute task generation workflow:

    • Load plan.md and extract tech stack, libraries, project structure
    • Load spec.md and extract user stories with their priorities (P1, P2, P3, etc.)
    • If data-model.md exists: Extract entities and map to user stories
    • If contracts/ exists: Map endpoints to user stories
    • If research.md exists: Extract decisions for setup tasks
    • Generate tasks organized by user story (see Task Generation Rules below)
    • Generate dependency graph showing user story completion order
    • Create parallel execution examples per user story
    • Validate task completeness (each user story has all needed tasks, independently testable)
  4. Generate tasks.md: Use templates/tasks-template.md as structure, fill with:

    • Correct feature name from plan.md
    • Phase 1: Setup tasks (project initialization)
    • Phase 2: Foundational tasks (blocking prerequisites for all user stories)
    • Phase 3+: One phase per user story (in priority order from spec.md)
    • Each phase includes: story goal, independent test criteria, tests (if requested), implementation tasks
    • Final Phase: Polish & cross-cutting concerns
    • All tasks must follow the strict checklist format (see Task Generation Rules below)
    • Clear file paths for each task
    • Dependencies section showing story completion order
    • Parallel execution examples per story
    • Implementation strategy section (MVP first, incremental delivery)
  5. Report: Output path to generated tasks.md and summary:

    • Total task count
    • Task count per user story
    • Parallel opportunities identified
    • Independent test criteria for each story
    • Suggested MVP scope (typically just User Story 1)
    • Format validation: Confirm ALL tasks follow the checklist format (checkbox, ID, labels, file paths)

Context for task generation: {{args}}

The tasks.md should be immediately executable - each task must be specific enough that an LLM can complete it without additional context.

Task Generation Rules

CRITICAL: Tasks MUST be organized by user story to enable independent implementation and testing.

Tests are OPTIONAL: Only generate test tasks if explicitly requested in the feature specification or if user requests TDD approach.

Checklist Format (REQUIRED)

Every task MUST strictly follow this format:

text
- [ ] [TaskID] [P?] [Story?] Description with file path

Format Components:

  1. Checkbox: ALWAYS start with - [ ] (markdown checkbox)
  2. Task ID: Sequential number (T001, T002, T003...) in execution order
  3. [P] marker: Include ONLY if task is parallelizable (different files, no dependencies on incomplete tasks)
  4. [Story] label: REQUIRED for user story phase tasks only
    • Format: [US1], [US2], [US3], etc. (maps to user stories from spec.md)
    • Setup phase: NO story label
    • Foundational phase: NO story label
    • User Story phases: MUST have story label
    • Polish phase: NO story label
  5. Description: Clear action with exact file path

Examples:

  • ✅ CORRECT: - [ ] T001 Create project structure per implementation plan
  • ✅ CORRECT: - [ ] T005 [P] Implement authentication middleware in src/middleware/auth.py
  • ✅ CORRECT: - [ ] T012 [P] [US1] Create User model in src/models/user.py
  • ✅ CORRECT: - [ ] T014 [US1] Implement UserService in src/services/user_service.py
  • ❌ WRONG: - [ ] Create User model (missing ID and Story label)
  • ❌ WRONG: T001 [US1] Create model (missing checkbox)
  • ❌ WRONG: - [ ] [US1] Create User model (missing Task ID)
  • ❌ WRONG: - [ ] T001 [US1] Create model (missing file path)

Task Organization

  1. From User Stories (spec.md) - PRIMARY ORGANIZATION:

    • Each user story (P1, P2, P3...) gets its own phase
    • Map all related components to their story:
      • Models needed for that story
      • Services needed for that story
      • Endpoints/UI needed for that story
      • If tests requested: Tests specific to that story
    • Mark story dependencies (most stories should be independent)
  2. From Contracts:

    • Map each contract/endpoint → to the user story it serves
    • If tests requested: Each contract → contract test task [P] before implementation in that story's phase
  3. From Data Model:

    • Map each entity to the user story(ies) that need it
    • If entity serves multiple stories: Put in earliest story or Setup phase
    • Relationships → service layer tasks in appropriate story phase
  4. From Setup/Infrastructure:

    • Shared infrastructure → Setup phase (Phase 1)
    • Foundational/blocking tasks → Foundational phase (Phase 2)
    • Story-specific setup → within that story's phase

Phase Structure

  • Phase 1: Setup (project initialization)
  • Phase 2: Foundational (blocking prerequisites - MUST complete before user stories)
  • Phase 3+: User Stories in priority order (P1, P2, P3...)
    • Within each story: Tests (if requested) → Models → Services → Endpoints → Integration
    • Each phase should be a complete, independently testable increment
  • Final Phase: Polish & Cross-Cutting Concerns

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Speckit.Tasks AI skill do?

Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

Why use Speckit.Tasks on TypingMind?

Because you install it once and use it with any model. Speckit.Tasks is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Speckit.Tasks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/compnew2006/Spec-Kit-Antigravity-Skills/tree/main/skills/speckit.tasks. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Speckit.Tasks?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Speckit.Tasks?

As many as you like. As long as a model supports skills, you can use Speckit.Tasks with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Speckit.Tasks AI skill free?

It is published on GitHub by compnew2006. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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